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Forecasting of Short Time Series with Intelligent Computing

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Challenging Problems and Solutions in Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 634))

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Abstract

Although time series analysis and forecasting have been studied since the seventeenth century and the literature related to its statistical foundations is extensive, the problem arises when the assumptions underlying statistical modeling are not fulfilled due to the shortness of available data. In such cases, additional expert knowledge is needed to support the forecasting process. The inclusion of prior knowledge may be easily formalized with the Bayesian approach. However, the proper formulation of prior probability distributions is still one of the main challenges for practitioners. Hopefully, intelligent computing can support the formulation of the prior knowledge. In this paper, we review recent trends and challenges of the interdisciplinary research on time series forecasting with the use of intelligent computing, especially fuzzy systems. Then, we propose a method that incorporates fuzzy trends and linguistic summaries for the forecasting of short time series. Experiments show that it is a very promising and human-consistent approach.

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Acknowledgments

Katarzyna Kaczmarek is supported by the Foundation for Polish Science under International Ph.D. Projects in Intelligent Computing financed from the European Union within the Innovative Economy Operational Programme 2007–2013 and European Regional Development Fund.

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Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Katarzyna Kaczmarek & Olgierd Hryniewicz

Authors
  1. Katarzyna Kaczmarek
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  2. Olgierd Hryniewicz
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Corresponding author

Correspondence to Katarzyna Kaczmarek .

Editor information

Editors and Affiliations

  1. Dept. of Telecommunication & Inform proc, Ghent University, Gent, Belgium

    Guy de Trė

  2. Faculty of Maths and Information Science, Warsaw University of Technology, Warszawa, Poland

    Przemysław Grzegorzewski

  3. Polish Academy of Sciences, Systems Research Institute, Warszawa, Poland

    Janusz Kacprzyk

  4. Polish Academy of Sciences, Systems Research Institute, Warszawa, Poland

    Jan W. Owsiński

  5. Polish Academy of Sciences, Institute of Computer Science, Warszawa, Poland

    Wojciech Penczek

  6. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Sławomir Zadrożny

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Kaczmarek, K., Hryniewicz, O. (2016). Forecasting of Short Time Series with Intelligent Computing. In: Trė, G., Grzegorzewski, P., Kacprzyk, J., Owsiński, J., Penczek, W., Zadrożny, S. (eds) Challenging Problems and Solutions in Intelligent Systems. Studies in Computational Intelligence, vol 634. Springer, Cham. https://doi.org/10.1007/978-3-319-30165-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-30165-5_4

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